Unverified50% confidenceFactExact time
LLMs lack prior knowledge of specific business contexts and struggle to build cross-module state-machine reasoning, so they easily miss business logic vulnerabilities.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
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Related Claims
UnverifiedLLM本质上是条件生成器，需要清晰的问题边界才能高效运作，不擅长发现值得解决的问题78% similarUnverifiedLLMs are strictly speaking non-deterministic token generators; they do not understand causality, system boundaries or verification protocols.75% similarUnverifiedLarge models inherently lack prior knowledge of specific enterprise business logic, and the semantic layer serves as a domain dictionary for the model, enabling it to understand the specific meaning of business terminology.75% similarPartially VerifiedEach LLM inference is stateless and constrained by the limited context window; the model has no persistent memory of its own.75% similarVerifiedWhen requirement descriptions are vague, LLMs tend to over-engineer and generate complex enterprise-level architectural solutions, stemming from their text-continuation characteristic based on conditional probability rather than engineering judgment.74% similar
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